Overview

Dataset statistics

Number of variables18
Number of observations1629
Missing cells2229
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory229.2 KiB
Average record size in memory144.1 B

Variable types

Text14
Categorical1
Numeric3

Alerts

is_adult has constant value ""Constant
poster_path has 103 (6.3%) missing valuesMissing
story has 20 (1.2%) missing valuesMissing
tagline has 1072 (65.8%) missing valuesMissing
wins_nominations has 922 (56.6%) missing valuesMissing
release_date has 107 (6.6%) missing valuesMissing
wiki_link has unique valuesUnique

Reproduction

Analysis started2023-11-12 15:23:57.316186
Analysis finished2023-11-12 15:24:00.299398
Duration2.98 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Distinct1625
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
2023-11-12T20:54:00.517093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length30
Mean length16.04911
Min length3

Characters and Unicode

Total characters26144
Distinct characters79
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1621 ?
Unique (%)99.5%

Sample

1st rowUri: The Surgical Strike
2nd rowBattalion 609
3rd rowThe Accidental Prime Minister (film)
4th rowWhy Cheat India
5th rowEvening Shadows
ValueCountFrequency (%)
film 462
 
9.9%
the 95
 
2.0%
hai 54
 
1.2%
of 37
 
0.8%
ki 37
 
0.8%
ek 35
 
0.7%
dil 34
 
0.7%
2 31
 
0.7%
love 30
 
0.6%
pyaar 26
 
0.6%
Other values (2291) 3829
82.0%
2023-11-12T20:54:01.001384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3041
 
11.6%
a 3008
 
11.5%
i 1780
 
6.8%
e 1420
 
5.4%
n 1062
 
4.1%
l 1037
 
4.0%
h 1020
 
3.9%
r 988
 
3.8%
o 928
 
3.5%
m 836
 
3.2%
Other values (69) 11024
42.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16831
64.4%
Uppercase Letter 3771
 
14.4%
Space Separator 3041
 
11.6%
Decimal Number 1292
 
4.9%
Close Punctuation 460
 
1.8%
Open Punctuation 460
 
1.8%
Other Punctuation 251
 
1.0%
Dash Punctuation 37
 
0.1%
Other Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3008
17.9%
i 1780
10.6%
e 1420
 
8.4%
n 1062
 
6.3%
l 1037
 
6.2%
h 1020
 
6.1%
r 988
 
5.9%
o 928
 
5.5%
m 836
 
5.0%
t 654
 
3.9%
Other values (18) 4098
24.3%
Uppercase Letter
ValueCountFrequency (%)
K 321
 
8.5%
S 318
 
8.4%
M 305
 
8.1%
B 275
 
7.3%
H 268
 
7.1%
D 266
 
7.1%
T 255
 
6.8%
A 228
 
6.0%
P 213
 
5.6%
C 154
 
4.1%
Other values (16) 1168
31.0%
Decimal Number
ValueCountFrequency (%)
0 470
36.4%
2 356
27.6%
1 180
 
13.9%
3 55
 
4.3%
4 46
 
3.6%
5 45
 
3.5%
6 41
 
3.2%
7 38
 
2.9%
9 35
 
2.7%
8 26
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 97
38.6%
: 97
38.6%
! 27
 
10.8%
' 11
 
4.4%
? 10
 
4.0%
& 5
 
2.0%
@ 2
 
0.8%
/ 1
 
0.4%
… 1
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 23
62.2%
– 14
37.8%
Space Separator
ValueCountFrequency (%)
3041
100.0%
Close Punctuation
ValueCountFrequency (%)
) 460
100.0%
Open Punctuation
ValueCountFrequency (%)
( 460
100.0%
Other Number
ValueCountFrequency (%)
½ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20602
78.8%
Common 5542
 
21.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3008
 
14.6%
i 1780
 
8.6%
e 1420
 
6.9%
n 1062
 
5.2%
l 1037
 
5.0%
h 1020
 
5.0%
r 988
 
4.8%
o 928
 
4.5%
m 836
 
4.1%
t 654
 
3.2%
Other values (44) 7869
38.2%
Common
ValueCountFrequency (%)
3041
54.9%
0 470
 
8.5%
) 460
 
8.3%
( 460
 
8.3%
2 356
 
6.4%
1 180
 
3.2%
. 97
 
1.8%
: 97
 
1.8%
3 55
 
1.0%
4 46
 
0.8%
Other values (15) 280
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26126
99.9%
Punctuation 15
 
0.1%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3041
 
11.6%
a 3008
 
11.5%
i 1780
 
6.8%
e 1420
 
5.4%
n 1062
 
4.1%
l 1037
 
4.0%
h 1020
 
3.9%
r 988
 
3.8%
o 928
 
3.6%
m 836
 
3.2%
Other values (64) 11006
42.1%
Punctuation
ValueCountFrequency (%)
– 14
93.3%
… 1
 
6.7%
None
ValueCountFrequency (%)
½ 1
33.3%
Å› 1
33.3%
é 1
33.3%
Distinct1623
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
2023-11-12T20:54:01.331740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.0055249
Min length9

Characters and Unicode

Total characters14670
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1617 ?
Unique (%)99.3%

Sample

1st rowtt8291224
2nd rowtt9472208
3rd rowtt6986710
4th rowtt8108208
5th rowtt6028796
ValueCountFrequency (%)
tt2140465 2
 
0.1%
tt1954470 2
 
0.1%
tt0346507 2
 
0.1%
tt2424988 2
 
0.1%
tt0347416 2
 
0.1%
tt0169102 2
 
0.1%
tt5013008 1
 
0.1%
tt8396186 1
 
0.1%
tt7777196 1
 
0.1%
tt6903440 1
 
0.1%
Other values (1613) 1613
99.0%
2023-11-12T20:54:01.788027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 3258
22.2%
0 1524
10.4%
4 1342
9.1%
2 1249
 
8.5%
1 1241
 
8.5%
3 1194
 
8.1%
8 1091
 
7.4%
6 1026
 
7.0%
7 984
 
6.7%
9 882
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11412
77.8%
Lowercase Letter 3258
 
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1524
13.4%
4 1342
11.8%
2 1249
10.9%
1 1241
10.9%
3 1194
10.5%
8 1091
9.6%
6 1026
9.0%
7 984
8.6%
9 882
7.7%
5 879
7.7%
Lowercase Letter
ValueCountFrequency (%)
t 3258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11412
77.8%
Latin 3258
 
22.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1524
13.4%
4 1342
11.8%
2 1249
10.9%
1 1241
10.9%
3 1194
10.5%
8 1091
9.6%
6 1026
9.0%
7 984
8.6%
9 882
7.7%
5 879
7.7%
Latin
ValueCountFrequency (%)
t 3258
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3258
22.2%
0 1524
10.4%
4 1342
9.1%
2 1249
 
8.5%
1 1241
 
8.5%
3 1194
 
8.1%
8 1091
 
7.4%
6 1026
 
7.0%
7 984
 
6.7%
9 882
 
6.0%

poster_path
Text

MISSING 

Distinct1517
Distinct (%)99.4%
Missing103
Missing (%)6.3%
Memory size12.9 KiB
2023-11-12T20:54:02.047261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length170
Median length148
Mean length104.11206
Min length57

Characters and Unicode

Total characters158875
Distinct characters68
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1511 ?
Unique (%)99.0%

Sample

1st rowhttps://upload.wikimedia.org/wikipedia/en/thumb/3/3b/URI_-_New_poster.jpg/220px-URI_-_New_poster.jpg
2nd rowhttps://upload.wikimedia.org/wikipedia/en/thumb/a/a1/The_Accidental_Prime_Minister_film.jpg/220px-The_Accidental_Prime_Minister_film.jpg
3rd rowhttps://upload.wikimedia.org/wikipedia/en/thumb/a/a2/Why_Cheat_India_poster.jpg/220px-Why_Cheat_India_poster.jpg
4th rowhttps://upload.wikimedia.org/wikipedia/en/thumb/d/de/Soni_India_Netflix_Movie_Poster.jpg/220px-Soni_India_Netflix_Movie_Poster.jpg
5th rowhttps://upload.wikimedia.org/wikipedia/en/thumb/4/48/Fraud_Saiyyan_film_poster.jpg/220px-Fraud_Saiyyan_film_poster.jpg
ValueCountFrequency (%)
https://upload.wikimedia.org/wikipedia/en/thumb/4/41/flag_of_india.svg/23px-flag_of_india.svg.png 4
 
0.3%
https://upload.wikimedia.org/wikipedia/en/thumb/4/41/flag_of_india.svg/32px-flag_of_india.svg.png 3
 
0.2%
https://upload.wikimedia.org/wikipedia/en/thumb/b/b6/lagaan.jpg/220px-lagaan.jpg 2
 
0.1%
https://upload.wikimedia.org/wikipedia/en/thumb/c/c7/gabbar_is_back_first_look.jpg/220px-gabbar_is_back_first_look.jpg 2
 
0.1%
https://upload.wikimedia.org/wikipedia/en/thumb/3/3f/tanu_weds_manu_poster.jpg/220px-tanu_weds_manu_poster.jpg 2
 
0.1%
https://upload.wikimedia.org/wikipedia/en/thumb/3/39/andaaz_movieposter.jpg/220px-andaaz_movieposter.jpg 2
 
0.1%
https://upload.wikimedia.org/wikipedia/en/thumb/4/46/sonchiriya_poster.jpg/220px-sonchiriya_poster.jpg 1
 
0.1%
https://upload.wikimedia.org/wikipedia/en/thumb/d/de/soni_india_netflix_movie_poster.jpg/220px-soni_india_netflix_movie_poster.jpg 1
 
0.1%
https://upload.wikimedia.org/wikipedia/en/thumb/4/48/fraud_saiyyan_film_poster.jpg/220px-fraud_saiyyan_film_poster.jpg 1
 
0.1%
https://upload.wikimedia.org/wikipedia/en/thumb/b/bb/bombairiya_poster.jpg/220px-bombairiya_poster.jpg 1
 
0.1%
Other values (1507) 1507
98.8%
2023-11-12T20:54:02.464163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 13462
 
8.5%
i 12413
 
7.8%
a 10223
 
6.4%
p 10020
 
6.3%
e 9248
 
5.8%
t 7273
 
4.6%
o 6680
 
4.2%
. 6067
 
3.8%
_ 6024
 
3.8%
d 5704
 
3.6%
Other values (58) 71761
45.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111691
70.3%
Other Punctuation 21643
 
13.6%
Decimal Number 10042
 
6.3%
Uppercase Letter 7598
 
4.8%
Connector Punctuation 6024
 
3.8%
Dash Punctuation 1877
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 12413
 
11.1%
a 10223
 
9.2%
p 10020
 
9.0%
e 9248
 
8.3%
t 7273
 
6.5%
o 6680
 
6.0%
d 5704
 
5.1%
r 4948
 
4.4%
g 4886
 
4.4%
h 4689
 
4.2%
Other values (16) 35607
31.9%
Uppercase Letter
ValueCountFrequency (%)
P 1077
14.2%
M 650
 
8.6%
S 506
 
6.7%
K 502
 
6.6%
D 480
 
6.3%
B 455
 
6.0%
H 441
 
5.8%
T 423
 
5.6%
A 410
 
5.4%
C 314
 
4.1%
Other values (16) 2340
30.8%
Decimal Number
ValueCountFrequency (%)
2 4133
41.2%
0 2300
22.9%
1 626
 
6.2%
8 565
 
5.6%
9 545
 
5.4%
3 427
 
4.3%
7 406
 
4.0%
6 361
 
3.6%
4 355
 
3.5%
5 324
 
3.2%
Other Punctuation
ValueCountFrequency (%)
/ 13462
62.2%
. 6067
28.0%
: 1526
 
7.1%
% 588
 
2.7%
Connector Punctuation
ValueCountFrequency (%)
_ 6024
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1877
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119289
75.1%
Common 39586
 
24.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 12413
 
10.4%
a 10223
 
8.6%
p 10020
 
8.4%
e 9248
 
7.8%
t 7273
 
6.1%
o 6680
 
5.6%
d 5704
 
4.8%
r 4948
 
4.1%
g 4886
 
4.1%
h 4689
 
3.9%
Other values (42) 43205
36.2%
Common
ValueCountFrequency (%)
/ 13462
34.0%
. 6067
15.3%
_ 6024
15.2%
2 4133
 
10.4%
0 2300
 
5.8%
- 1877
 
4.7%
: 1526
 
3.9%
1 626
 
1.6%
% 588
 
1.5%
8 565
 
1.4%
Other values (6) 2418
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 13462
 
8.5%
i 12413
 
7.8%
a 10223
 
6.4%
p 10020
 
6.3%
e 9248
 
5.8%
t 7273
 
4.6%
o 6680
 
4.2%
. 6067
 
3.8%
_ 6024
 
3.8%
d 5704
 
3.6%
Other values (58) 71761
45.2%

wiki_link
Text

UNIQUE 

Distinct1629
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
2023-11-12T20:54:02.718076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length62
Mean length46.347452
Min length33

Characters and Unicode

Total characters75500
Distinct characters72
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1629 ?
Unique (%)100.0%

Sample

1st rowhttps://en.wikipedia.org/wiki/Uri:_The_Surgical_Strike
2nd rowhttps://en.wikipedia.org/wiki/Battalion_609
3rd rowhttps://en.wikipedia.org/wiki/The_Accidental_Prime_Minister_(film)
4th rowhttps://en.wikipedia.org/wiki/Why_Cheat_India
5th rowhttps://en.wikipedia.org/wiki/Evening_Shadows
ValueCountFrequency (%)
https://en.wikipedia.org/wiki/gabbar_is_back 2
 
0.1%
https://en.wikipedia.org/wiki/uri:_the_surgical_strike 1
 
0.1%
https://en.wikipedia.org/wiki/gone_kesh 1
 
0.1%
https://en.wikipedia.org/wiki/the_accidental_prime_minister_(film 1
 
0.1%
https://en.wikipedia.org/wiki/why_cheat_india 1
 
0.1%
https://en.wikipedia.org/wiki/evening_shadows 1
 
0.1%
https://en.wikipedia.org/wiki/soni_(film 1
 
0.1%
https://en.wikipedia.org/wiki/fraud_saiyaan 1
 
0.1%
https://en.wikipedia.org/wiki/bombairiya 1
 
0.1%
https://en.wikipedia.org/wiki/manikarnika:_the_queen_of_jhansi 1
 
0.1%
Other values (1618) 1618
99.3%
2023-11-12T20:54:03.367792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 9950
 
13.2%
/ 6518
 
8.6%
e 4674
 
6.2%
a 4637
 
6.1%
t 3916
 
5.2%
k 3569
 
4.7%
p 3403
 
4.5%
w 3368
 
4.5%
. 3353
 
4.4%
_ 3096
 
4.1%
Other values (62) 29016
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54396
72.0%
Other Punctuation 11690
 
15.5%
Uppercase Letter 3816
 
5.1%
Connector Punctuation 3096
 
4.1%
Decimal Number 1504
 
2.0%
Close Punctuation 486
 
0.6%
Open Punctuation 486
 
0.6%
Dash Punctuation 26
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9950
18.3%
e 4674
 
8.6%
a 4637
 
8.5%
t 3916
 
7.2%
k 3569
 
6.6%
p 3403
 
6.3%
w 3368
 
6.2%
n 2698
 
5.0%
h 2654
 
4.9%
r 2611
 
4.8%
Other values (16) 12916
23.7%
Uppercase Letter
ValueCountFrequency (%)
K 320
 
8.4%
S 318
 
8.3%
M 303
 
7.9%
B 277
 
7.3%
H 265
 
6.9%
D 264
 
6.9%
T 260
 
6.8%
A 232
 
6.1%
P 212
 
5.6%
C 160
 
4.2%
Other values (16) 1205
31.6%
Decimal Number
ValueCountFrequency (%)
0 512
34.0%
2 411
27.3%
1 202
 
13.4%
3 86
 
5.7%
7 55
 
3.7%
9 52
 
3.5%
5 49
 
3.3%
4 48
 
3.2%
6 47
 
3.1%
8 42
 
2.8%
Other Punctuation
ValueCountFrequency (%)
/ 6518
55.8%
. 3353
28.7%
: 1720
 
14.7%
% 73
 
0.6%
! 24
 
0.2%
@ 2
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 3096
100.0%
Close Punctuation
ValueCountFrequency (%)
) 486
100.0%
Open Punctuation
ValueCountFrequency (%)
( 486
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58212
77.1%
Common 17288
 
22.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9950
17.1%
e 4674
 
8.0%
a 4637
 
8.0%
t 3916
 
6.7%
k 3569
 
6.1%
p 3403
 
5.8%
w 3368
 
5.8%
n 2698
 
4.6%
h 2654
 
4.6%
r 2611
 
4.5%
Other values (42) 16732
28.7%
Common
ValueCountFrequency (%)
/ 6518
37.7%
. 3353
19.4%
_ 3096
17.9%
: 1720
 
9.9%
0 512
 
3.0%
) 486
 
2.8%
( 486
 
2.8%
2 411
 
2.4%
1 202
 
1.2%
3 86
 
0.5%
Other values (10) 418
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9950
 
13.2%
/ 6518
 
8.6%
e 4674
 
6.2%
a 4637
 
6.1%
t 3916
 
5.2%
k 3569
 
4.7%
p 3403
 
4.5%
w 3368
 
4.5%
. 3353
 
4.4%
_ 3096
 
4.1%
Other values (62) 29016
38.4%
Distinct1620
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
2023-11-12T20:54:03.670839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length37
Mean length13.901166
Min length1

Characters and Unicode

Total characters22645
Distinct characters75
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1612 ?
Unique (%)99.0%

Sample

1st rowUri: The Surgical Strike
2nd rowBattalion 609
3rd rowThe Accidental Prime Minister
4th rowWhy Cheat India
5th rowEvening Shadows
ValueCountFrequency (%)
the 117
 
2.8%
hai 54
 
1.3%
of 51
 
1.2%
ki 37
 
0.9%
ek 36
 
0.9%
dil 35
 
0.8%
love 34
 
0.8%
2 32
 
0.8%
a 31
 
0.8%
pyaar 26
 
0.6%
Other values (2338) 3671
89.0%
2023-11-12T20:54:04.240118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3013
 
13.3%
2495
 
11.0%
e 1545
 
6.8%
i 1331
 
5.9%
n 1108
 
4.9%
h 1053
 
4.7%
r 1043
 
4.6%
o 1019
 
4.5%
t 720
 
3.2%
s 617
 
2.7%
Other values (65) 8701
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15632
69.0%
Uppercase Letter 3924
 
17.3%
Space Separator 2495
 
11.0%
Other Punctuation 402
 
1.8%
Decimal Number 161
 
0.7%
Dash Punctuation 27
 
0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3013
19.3%
e 1545
9.9%
i 1331
 
8.5%
n 1108
 
7.1%
h 1053
 
6.7%
r 1043
 
6.7%
o 1019
 
6.5%
t 720
 
4.6%
s 617
 
3.9%
l 612
 
3.9%
Other values (16) 3571
22.8%
Uppercase Letter
ValueCountFrequency (%)
S 334
 
8.5%
K 316
 
8.1%
M 307
 
7.8%
B 289
 
7.4%
T 271
 
6.9%
D 269
 
6.9%
H 254
 
6.5%
A 224
 
5.7%
P 216
 
5.5%
C 167
 
4.3%
Other values (16) 1277
32.5%
Decimal Number
ValueCountFrequency (%)
2 50
31.1%
3 25
15.5%
1 22
13.7%
0 17
 
10.6%
9 13
 
8.1%
5 9
 
5.6%
4 8
 
5.0%
6 6
 
3.7%
8 6
 
3.7%
7 5
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 164
40.8%
: 147
36.6%
! 45
 
11.2%
' 25
 
6.2%
? 10
 
2.5%
& 5
 
1.2%
/ 3
 
0.7%
@ 2
 
0.5%
# 1
 
0.2%
Space Separator
ValueCountFrequency (%)
2495
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19556
86.4%
Common 3089
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3013
15.4%
e 1545
 
7.9%
i 1331
 
6.8%
n 1108
 
5.7%
h 1053
 
5.4%
r 1043
 
5.3%
o 1019
 
5.2%
t 720
 
3.7%
s 617
 
3.2%
l 612
 
3.1%
Other values (42) 7495
38.3%
Common
ValueCountFrequency (%)
2495
80.8%
. 164
 
5.3%
: 147
 
4.8%
2 50
 
1.6%
! 45
 
1.5%
- 27
 
0.9%
' 25
 
0.8%
3 25
 
0.8%
1 22
 
0.7%
0 17
 
0.6%
Other values (13) 72
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22645
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3013
 
13.3%
2495
 
11.0%
e 1545
 
6.8%
i 1331
 
5.9%
n 1108
 
4.9%
h 1053
 
4.7%
r 1043
 
4.6%
o 1019
 
4.5%
t 720
 
3.2%
s 617
 
2.7%
Other values (65) 8701
38.4%
Distinct1621
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
2023-11-12T20:54:04.549560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length40
Mean length13.910988
Min length1

Characters and Unicode

Total characters22661
Distinct characters75
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1613 ?
Unique (%)99.0%

Sample

1st rowUri: The Surgical Strike
2nd rowBattalion 609
3rd rowThe Accidental Prime Minister
4th rowWhy Cheat India
5th rowEvening Shadows
ValueCountFrequency (%)
the 107
 
2.6%
hai 55
 
1.3%
of 45
 
1.1%
ki 39
 
0.9%
ek 37
 
0.9%
dil 35
 
0.9%
love 32
 
0.8%
a 31
 
0.8%
2 30
 
0.7%
pyaar 26
 
0.6%
Other values (2346) 3679
89.4%
2023-11-12T20:54:05.122627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3085
 
13.6%
2487
 
11.0%
e 1522
 
6.7%
i 1334
 
5.9%
n 1107
 
4.9%
h 1056
 
4.7%
r 1029
 
4.5%
o 1012
 
4.5%
t 707
 
3.1%
l 607
 
2.7%
Other values (65) 8715
38.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15642
69.0%
Uppercase Letter 3932
 
17.4%
Space Separator 2487
 
11.0%
Other Punctuation 408
 
1.8%
Decimal Number 160
 
0.7%
Dash Punctuation 30
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3085
19.7%
e 1522
9.7%
i 1334
 
8.5%
n 1107
 
7.1%
h 1056
 
6.8%
r 1029
 
6.6%
o 1012
 
6.5%
t 707
 
4.5%
l 607
 
3.9%
s 603
 
3.9%
Other values (16) 3580
22.9%
Uppercase Letter
ValueCountFrequency (%)
S 329
 
8.4%
K 325
 
8.3%
M 314
 
8.0%
B 291
 
7.4%
D 277
 
7.0%
T 267
 
6.8%
H 257
 
6.5%
A 234
 
6.0%
P 214
 
5.4%
C 164
 
4.2%
Other values (16) 1260
32.0%
Decimal Number
ValueCountFrequency (%)
2 48
30.0%
3 26
16.2%
1 22
13.8%
0 17
 
10.6%
9 13
 
8.1%
5 9
 
5.6%
4 8
 
5.0%
8 6
 
3.8%
7 6
 
3.8%
6 5
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 170
41.7%
: 154
37.7%
! 44
 
10.8%
' 20
 
4.9%
? 9
 
2.2%
& 5
 
1.2%
/ 3
 
0.7%
@ 2
 
0.5%
# 1
 
0.2%
Space Separator
ValueCountFrequency (%)
2487
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19574
86.4%
Common 3087
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3085
15.8%
e 1522
 
7.8%
i 1334
 
6.8%
n 1107
 
5.7%
h 1056
 
5.4%
r 1029
 
5.3%
o 1012
 
5.2%
t 707
 
3.6%
l 607
 
3.1%
s 603
 
3.1%
Other values (42) 7512
38.4%
Common
ValueCountFrequency (%)
2487
80.6%
. 170
 
5.5%
: 154
 
5.0%
2 48
 
1.6%
! 44
 
1.4%
- 30
 
1.0%
3 26
 
0.8%
1 22
 
0.7%
' 20
 
0.6%
0 17
 
0.6%
Other values (13) 69
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22661
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3085
 
13.6%
2487
 
11.0%
e 1522
 
6.7%
i 1334
 
5.9%
n 1107
 
4.9%
h 1056
 
4.7%
r 1029
 
4.5%
o 1012
 
4.5%
t 707
 
3.1%
l 607
 
2.7%
Other values (65) 8715
38.5%

is_adult
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
0
1629 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1629
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1629
100.0%

Length

2023-11-12T20:54:05.306074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-12T20:54:05.435396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1629
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1629
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1629
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1629
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1629
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1629
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1629
100.0%

year_of_release
Real number (ℝ)

Distinct19
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.264
Minimum2001
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-11-12T20:54:05.552886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2002
Q12005
median2011
Q32015
95-th percentile2018
Maximum2019
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.3815418
Coefficient of variation (CV)0.0026770324
Kurtosis-1.2306189
Mean2010.264
Median Absolute Deviation (MAD)5
Skewness-0.080667275
Sum3274720
Variance28.960992
MonotonicityNot monotonic
2023-11-12T20:54:05.709575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2014 110
 
6.8%
2015 105
 
6.4%
2003 99
 
6.1%
2005 97
 
6.0%
2017 96
 
5.9%
2016 94
 
5.8%
2013 94
 
5.8%
2010 92
 
5.6%
2006 85
 
5.2%
2011 84
 
5.2%
Other values (9) 673
41.3%
ValueCountFrequency (%)
2001 61
3.7%
2002 76
4.7%
2003 99
6.1%
2004 79
4.8%
2005 97
6.0%
2006 85
5.2%
2007 84
5.2%
2008 73
4.5%
2009 63
3.9%
2010 92
5.6%
ValueCountFrequency (%)
2019 75
4.6%
2018 79
4.8%
2017 96
5.9%
2016 94
5.8%
2015 105
6.4%
2014 110
6.8%
2013 94
5.8%
2012 83
5.1%
2011 84
5.2%
2010 92
5.6%
Distinct130
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
2023-11-12T20:54:05.907132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8637201
Min length2

Characters and Unicode

Total characters4665
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)1.6%

Sample

1st row138
2nd row131
3rd row112
4th row121
5th row102
ValueCountFrequency (%)
n 119
 
7.3%
120 56
 
3.4%
130 42
 
2.6%
135 41
 
2.5%
140 36
 
2.2%
137 34
 
2.1%
128 32
 
2.0%
138 31
 
1.9%
150 31
 
1.9%
122 29
 
1.8%
Other values (120) 1178
72.3%
2023-11-12T20:54:06.343595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1667
35.7%
2 430
 
9.2%
0 425
 
9.1%
3 423
 
9.1%
5 364
 
7.8%
4 332
 
7.1%
6 222
 
4.8%
9 210
 
4.5%
8 190
 
4.1%
7 164
 
3.5%
Other values (2) 238
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4427
94.9%
Other Punctuation 119
 
2.6%
Uppercase Letter 119
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1667
37.7%
2 430
 
9.7%
0 425
 
9.6%
3 423
 
9.6%
5 364
 
8.2%
4 332
 
7.5%
6 222
 
5.0%
9 210
 
4.7%
8 190
 
4.3%
7 164
 
3.7%
Other Punctuation
ValueCountFrequency (%)
\ 119
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 119
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4546
97.4%
Latin 119
 
2.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1667
36.7%
2 430
 
9.5%
0 425
 
9.3%
3 423
 
9.3%
5 364
 
8.0%
4 332
 
7.3%
6 222
 
4.9%
9 210
 
4.6%
8 190
 
4.2%
7 164
 
3.6%
Latin
ValueCountFrequency (%)
N 119
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4665
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1667
35.7%
2 430
 
9.2%
0 425
 
9.1%
3 423
 
9.1%
5 364
 
7.8%
4 332
 
7.1%
6 222
 
4.8%
9 210
 
4.5%
8 190
 
4.1%
7 164
 
3.5%
Other values (2) 238
 
5.1%

genres
Text

Distinct205
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
2023-11-12T20:54:06.573933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length21
Mean length15.087784
Min length3

Characters and Unicode

Total characters24578
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)5.3%

Sample

1st rowAction|Drama|War
2nd rowWar
3rd rowBiography|Drama
4th rowCrime|Drama
5th rowDrama
ValueCountFrequency (%)
drama 162
 
9.9%
comedy|drama|romance 101
 
6.2%
comedy|drama 88
 
5.4%
drama|romance 86
 
5.3%
action|crime|drama 86
 
5.3%
comedy 73
 
4.5%
comedy|romance 69
 
4.2%
action|thriller 36
 
2.2%
drama|musical|romance 28
 
1.7%
comedy|crime|drama 27
 
1.7%
Other values (195) 873
53.6%
2023-11-12T20:54:07.051296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2940
12.0%
m 2477
 
10.1%
r 2416
 
9.8%
| 2086
 
8.5%
e 1815
 
7.4%
o 1718
 
7.0%
i 1308
 
5.3%
D 1090
 
4.4%
n 1012
 
4.1%
c 1012
 
4.1%
Other values (21) 6704
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18747
76.3%
Uppercase Letter 3730
 
15.2%
Math Symbol 2086
 
8.5%
Dash Punctuation 15
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2940
15.7%
m 2477
13.2%
r 2416
12.9%
e 1815
9.7%
o 1718
9.2%
i 1308
7.0%
n 1012
 
5.4%
c 1012
 
5.4%
y 987
 
5.3%
t 726
 
3.9%
Other values (8) 2336
12.5%
Uppercase Letter
ValueCountFrequency (%)
D 1090
29.2%
C 850
22.8%
A 507
13.6%
R 451
12.1%
T 274
 
7.3%
M 217
 
5.8%
F 126
 
3.4%
H 100
 
2.7%
B 50
 
1.3%
S 49
 
1.3%
Math Symbol
ValueCountFrequency (%)
| 2086
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22477
91.5%
Common 2101
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2940
13.1%
m 2477
11.0%
r 2416
10.7%
e 1815
 
8.1%
o 1718
 
7.6%
i 1308
 
5.8%
D 1090
 
4.8%
n 1012
 
4.5%
c 1012
 
4.5%
y 987
 
4.4%
Other values (19) 5702
25.4%
Common
ValueCountFrequency (%)
| 2086
99.3%
- 15
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2940
12.0%
m 2477
 
10.1%
r 2416
 
9.8%
| 2086
 
8.5%
e 1815
 
7.4%
o 1718
 
7.0%
i 1308
 
5.3%
D 1090
 
4.4%
n 1012
 
4.1%
c 1012
 
4.1%
Other values (21) 6704
27.3%

imdb_rating
Real number (ℝ)

Distinct76
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5574586
Minimum0
Maximum9.4
Zeros9
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-11-12T20:54:07.249557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.9
Q14.4
median5.6
Q36.8
95-th percentile8
Maximum9.4
Range9.4
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation1.5676086
Coefficient of variation (CV)0.28207293
Kurtosis-0.11519309
Mean5.5574586
Median Absolute Deviation (MAD)1.2
Skewness-0.37195319
Sum9053.1
Variance2.4573967
MonotonicityNot monotonic
2023-11-12T20:54:07.426598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.7 54
 
3.3%
6.2 45
 
2.8%
5.3 45
 
2.8%
7.2 45
 
2.8%
6.8 43
 
2.6%
5.2 42
 
2.6%
4.6 42
 
2.6%
5.4 41
 
2.5%
5.1 39
 
2.4%
5.5 39
 
2.4%
Other values (66) 1194
73.3%
ValueCountFrequency (%)
0 9
0.6%
1.5 2
 
0.1%
1.7 4
0.2%
1.8 1
 
0.1%
1.9 3
 
0.2%
2 4
0.2%
2.1 3
 
0.2%
2.2 2
 
0.1%
2.3 7
0.4%
2.4 4
0.2%
ValueCountFrequency (%)
9.4 1
 
0.1%
9 1
 
0.1%
8.9 1
 
0.1%
8.7 2
 
0.1%
8.6 1
 
0.1%
8.5 1
 
0.1%
8.4 7
 
0.4%
8.3 6
 
0.4%
8.2 22
1.4%
8.1 18
1.1%

imdb_votes
Real number (ℝ)

Distinct1267
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5384.2634
Minimum0
Maximum310481
Zeros9
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-11-12T20:54:07.598700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28
Q1233
median1000
Q34287
95-th percentile24020.2
Maximum310481
Range310481
Interquartile range (IQR)4054

Descriptive statistics

Standard deviation14552.103
Coefficient of variation (CV)2.7027102
Kurtosis139.97855
Mean5384.2634
Median Absolute Deviation (MAD)925
Skewness9.0909717
Sum8770965
Variance2.1176371 × 108
MonotonicityNot monotonic
2023-11-12T20:54:07.785821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
0.6%
40 7
 
0.4%
39 6
 
0.4%
47 6
 
0.4%
43 6
 
0.4%
13 6
 
0.4%
67 5
 
0.3%
6 5
 
0.3%
53 5
 
0.3%
22 5
 
0.3%
Other values (1257) 1569
96.3%
ValueCountFrequency (%)
0 9
0.6%
5 2
 
0.1%
6 5
0.3%
7 4
0.2%
8 3
 
0.2%
9 2
 
0.1%
10 1
 
0.1%
11 3
 
0.2%
12 3
 
0.2%
13 6
0.4%
ValueCountFrequency (%)
310481 1
0.1%
148498 1
0.1%
143605 1
0.1%
131338 1
0.1%
117000 1
0.1%
103071 1
0.1%
95686 2
0.1%
92755 1
0.1%
76737 1
0.1%
71636 2
0.1%

story
Text

MISSING 

Distinct1603
Distinct (%)99.6%
Missing20
Missing (%)1.2%
Memory size12.9 KiB
2023-11-12T20:54:08.069368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13128
Median length807
Mean length622.94469
Min length27

Characters and Unicode

Total characters1002318
Distinct characters92
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1597 ?
Unique (%)99.3%

Sample

1st rowDivided over five chapters the film chronicles the events of the surgical strike conducted by the Indian military against suspected militants in Pakistan occupied Kashmir. It tells the story of the 11 tumultuous events over which the operation was carried out. Indian army special forces carry out a covert operation to avenge the killing of fellow army men at their base by a terrorist group.
2nd rowThe story revolves around a cricket match between the Indian Army and the Pakistan Army gone wrong and narrates the tale of the soldiers of Battalion 609 putting up a fight with Taliban. After an attack on Indian Army that is Battalion 609 near the LOC the match which was to be played between India and Pakistan is cancelled. Anwar Hussein a Pakistani soldier who is very fond of cricket and was looking forward to the match throws a cricket bat at the other end of the LOC and calls the Indian army and Indians a bunch of cowards. In anger the Indian army throws a ball towards them. Soon a verbal fight breaks between them which leads to the two teams deciding to play a cricket match. The losing team will take their post eighteen kilometres back.
3rd rowBased on the memoir by Indian policy analyst Sanjaya Baru The Accidental Prime Minister explores Manmohan Singh's tenure as the Prime Minister of India and the interference of Congress Party in contradicting his decisions during his first tenure. He details how Singh fell a victim to Congress Party's dynastic approach. It highlights how Manmohan Singh was constantly ignored by Congress to maintain turf for Rahul Gandhi the son of Sonia Gandhi and Rajiv Gandhi.
4th rowThe movie focuses on existing malpractices in country's education system the whole concept of buying your way through education jobs and earnings. Even with an evolving education system the country faces scams like SSC and HSC paper leaks CBSE re-examination Vyaapam etc. The movie tries to shift the attention of people to understand the vulnerability of hardworking and gifted students who get left out.
5th rowWhile gay rights and marriage equality has been embraced by most countries a small town in Southern India lives within a cocoon of traditions and social morality. In such a milieu when a young gay man Kartik comes out to his mother Vasudha her entire world comes crashing down. She has no one to turn to dispel her fears and doubts to understand her loving son's truth. Moreover as a woman trapped within a patriarchal conservative society her biggest challenge is to deal with her dogmatic husband Damodar and the conservative society around her. 'Evening Shadows' is a universal story about a mother-son bonding and its emotional strength to withstand the ravages of time and harsh realities.
ValueCountFrequency (%)
the 7443
 
4.3%
and 6432
 
3.7%
to 5979
 
3.4%
a 5666
 
3.3%
of 4013
 
2.3%
is 3572
 
2.1%
in 3350
 
1.9%
his 3013
 
1.7%
he 2216
 
1.3%
with 2119
 
1.2%
Other values (17080) 130010
74.8%
2023-11-12T20:54:08.581419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
182842
18.2%
e 88858
 
8.9%
a 74741
 
7.5%
t 63412
 
6.3%
i 61438
 
6.1%
n 56092
 
5.6%
o 52954
 
5.3%
s 52541
 
5.2%
h 48599
 
4.8%
r 48107
 
4.8%
Other values (82) 272734
27.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 769962
76.8%
Space Separator 182842
 
18.2%
Uppercase Letter 29750
 
3.0%
Other Punctuation 13389
 
1.3%
Dash Punctuation 2273
 
0.2%
Decimal Number 2021
 
0.2%
Close Punctuation 928
 
0.1%
Open Punctuation 925
 
0.1%
Math Symbol 112
 
< 0.1%
Connector Punctuation 100
 
< 0.1%
Other values (3) 16
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 88858
11.5%
a 74741
 
9.7%
t 63412
 
8.2%
i 61438
 
8.0%
n 56092
 
7.3%
o 52954
 
6.9%
s 52541
 
6.8%
h 48599
 
6.3%
r 48107
 
6.2%
l 32249
 
4.2%
Other values (21) 190971
24.8%
Uppercase Letter
ValueCountFrequency (%)
S 3653
12.3%
A 3176
 
10.7%
T 2246
 
7.5%
R 2091
 
7.0%
M 1998
 
6.7%
B 1788
 
6.0%
P 1571
 
5.3%
K 1561
 
5.2%
I 1476
 
5.0%
H 1382
 
4.6%
Other values (16) 8808
29.6%
Other Punctuation
ValueCountFrequency (%)
. 9620
71.9%
' 2121
 
15.8%
; 420
 
3.1%
" 271
 
2.0%
, 221
 
1.7%
/ 216
 
1.6%
? 208
 
1.6%
: 107
 
0.8%
& 92
 
0.7%
! 89
 
0.7%
Other values (3) 24
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 447
22.1%
1 393
19.4%
2 329
16.3%
9 164
 
8.1%
5 161
 
8.0%
3 140
 
6.9%
4 125
 
6.2%
8 92
 
4.6%
6 90
 
4.5%
7 80
 
4.0%
Close Punctuation
ValueCountFrequency (%)
) 916
98.7%
] 12
 
1.3%
Open Punctuation
ValueCountFrequency (%)
( 913
98.7%
[ 12
 
1.3%
Math Symbol
ValueCountFrequency (%)
| 109
97.3%
+ 3
 
2.7%
Space Separator
ValueCountFrequency (%)
182842
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2273
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 100
100.0%
Control
ValueCountFrequency (%)
13
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 799712
79.8%
Common 202606
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 88858
11.1%
a 74741
 
9.3%
t 63412
 
7.9%
i 61438
 
7.7%
n 56092
 
7.0%
o 52954
 
6.6%
s 52541
 
6.6%
h 48599
 
6.1%
r 48107
 
6.0%
l 32249
 
4.0%
Other values (47) 220721
27.6%
Common
ValueCountFrequency (%)
182842
90.2%
. 9620
 
4.7%
- 2273
 
1.1%
' 2121
 
1.0%
) 916
 
0.5%
( 913
 
0.5%
0 447
 
0.2%
; 420
 
0.2%
1 393
 
0.2%
2 329
 
0.2%
Other values (25) 2332
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1002288
> 99.9%
None 30
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
182842
18.2%
e 88858
 
8.9%
a 74741
 
7.5%
t 63412
 
6.3%
i 61438
 
6.1%
n 56092
 
5.6%
o 52954
 
5.3%
s 52541
 
5.2%
h 48599
 
4.8%
r 48107
 
4.8%
Other values (77) 272704
27.2%
None
ValueCountFrequency (%)
é 23
76.7%
ï 2
 
6.7%
è 2
 
6.7%
ê 2
 
6.7%
à 1
 
3.3%
Distinct1604
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
2023-11-12T20:54:08.914995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length422
Median length205
Mean length153.92265
Min length6

Characters and Unicode

Total characters250740
Distinct characters83
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1597 ?
Unique (%)98.0%

Sample

1st rowIndian army special forces execute a covert operation avenging the killing of fellow army men at their base by a terrorist group.
2nd rowThe story of Battalion 609 revolves around a cricket match between the Indian Army and the Pakistan army gone wrong and narrates the tale of the brave soldiers of Battalion 609 putting up a fight with the mighty Taliban.
3rd rowExplores Manmohan Singh's tenure as the Prime Minister of India and the kind of control he had over the cabinet and the country.
4th rowThe movie focuses on existing malpractices in country's education system the whole concept of buying your way through education jobs and earnings. Even with an evolving education system ...
5th rowUnder the 'Evening Shadows' truth often plays hide and seek. Set in South India and Mumbai 'Evening Shadows' is a tender heartwarming story about a mother-son bond that has to withstand the ravages of time distance and truths.
ValueCountFrequency (%)
a 2288
 
5.4%
the 1652
 
3.9%
to 1351
 
3.2%
and 1300
 
3.1%
of 1164
 
2.8%
in 981
 
2.3%
his 758
 
1.8%
is 740
 
1.8%
with 475
 
1.1%
403
 
1.0%
Other values (7431) 31031
73.6%
2023-11-12T20:54:09.436268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50560
20.2%
e 21579
 
8.6%
a 17536
 
7.0%
i 15231
 
6.1%
t 14659
 
5.8%
n 13828
 
5.5%
o 13544
 
5.4%
s 12733
 
5.1%
r 12411
 
4.9%
h 10301
 
4.1%
Other values (73) 68358
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 187946
75.0%
Space Separator 50582
 
20.2%
Uppercase Letter 6633
 
2.6%
Other Punctuation 4026
 
1.6%
Dash Punctuation 580
 
0.2%
Decimal Number 517
 
0.2%
Open Punctuation 220
 
0.1%
Close Punctuation 212
 
0.1%
Final Punctuation 22
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 21579
11.5%
a 17536
 
9.3%
i 15231
 
8.1%
t 14659
 
7.8%
n 13828
 
7.4%
o 13544
 
7.2%
s 12733
 
6.8%
r 12411
 
6.6%
h 10301
 
5.5%
l 8196
 
4.4%
Other values (18) 47928
25.5%
Uppercase Letter
ValueCountFrequency (%)
A 1134
17.1%
S 707
10.7%
T 554
 
8.4%
I 460
 
6.9%
M 404
 
6.1%
B 372
 
5.6%
R 357
 
5.4%
P 308
 
4.6%
K 302
 
4.6%
H 282
 
4.3%
Other values (15) 1753
26.4%
Other Punctuation
ValueCountFrequency (%)
. 3338
82.9%
' 464
 
11.5%
" 75
 
1.9%
? 42
 
1.0%
; 41
 
1.0%
: 17
 
0.4%
& 15
 
0.4%
/ 14
 
0.3%
! 13
 
0.3%
* 6
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 110
21.3%
0 106
20.5%
9 62
12.0%
2 56
10.8%
3 36
 
7.0%
5 36
 
7.0%
6 31
 
6.0%
7 31
 
6.0%
4 29
 
5.6%
8 20
 
3.9%
Space Separator
ValueCountFrequency (%)
50560
> 99.9%
  22
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 217
98.6%
[ 3
 
1.4%
Close Punctuation
ValueCountFrequency (%)
) 209
98.6%
] 3
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 580
100.0%
Final Punctuation
ValueCountFrequency (%)
» 22
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 194579
77.6%
Common 56161
 
22.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 21579
 
11.1%
a 17536
 
9.0%
i 15231
 
7.8%
t 14659
 
7.5%
n 13828
 
7.1%
o 13544
 
7.0%
s 12733
 
6.5%
r 12411
 
6.4%
h 10301
 
5.3%
l 8196
 
4.2%
Other values (43) 54561
28.0%
Common
ValueCountFrequency (%)
50560
90.0%
. 3338
 
5.9%
- 580
 
1.0%
' 464
 
0.8%
( 217
 
0.4%
) 209
 
0.4%
1 110
 
0.2%
0 106
 
0.2%
" 75
 
0.1%
9 62
 
0.1%
Other values (20) 440
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250690
> 99.9%
None 50
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50560
20.2%
e 21579
 
8.6%
a 17536
 
7.0%
i 15231
 
6.1%
t 14659
 
5.8%
n 13828
 
5.5%
o 13544
 
5.4%
s 12733
 
5.1%
r 12411
 
5.0%
h 10301
 
4.1%
Other values (69) 68308
27.2%
None
ValueCountFrequency (%)
  22
44.0%
» 22
44.0%
é 5
 
10.0%
ï 1
 
2.0%

tagline
Text

MISSING 

Distinct553
Distinct (%)99.3%
Missing1072
Missing (%)65.8%
Memory size12.9 KiB
2023-11-12T20:54:09.769745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length247
Median length79
Mean length35.096948
Min length8

Characters and Unicode

Total characters19549
Distinct characters78
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique549 ?
Unique (%)98.6%

Sample

1st rowThey didn't mean to change the world.
2nd rowApna Time Aayega!
3rd rowThe Wildest Adventure Ever
4th rowMeri Ek Arzi Sun Lo Zara
5th rowA Film for Every Indian
ValueCountFrequency (%)
the 173
 
4.8%
a 159
 
4.5%
love 97
 
2.7%
of 95
 
2.7%
to 67
 
1.9%
you 62
 
1.7%
is 59
 
1.7%
in 47
 
1.3%
story 45
 
1.3%
38
 
1.1%
Other values (1237) 2728
76.4%
2023-11-12T20:54:10.322384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3015
15.4%
e 1915
 
9.8%
o 1243
 
6.4%
a 1106
 
5.7%
t 1041
 
5.3%
i 1000
 
5.1%
n 970
 
5.0%
r 970
 
5.0%
s 832
 
4.3%
. 782
 
4.0%
Other values (68) 6675
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13797
70.6%
Space Separator 3015
 
15.4%
Uppercase Letter 1532
 
7.8%
Other Punctuation 967
 
4.9%
Math Symbol 105
 
0.5%
Decimal Number 100
 
0.5%
Dash Punctuation 19
 
0.1%
Open Punctuation 7
 
< 0.1%
Close Punctuation 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1915
13.9%
o 1243
 
9.0%
a 1106
 
8.0%
t 1041
 
7.5%
i 1000
 
7.2%
n 970
 
7.0%
r 970
 
7.0%
s 832
 
6.0%
h 676
 
4.9%
l 543
 
3.9%
Other values (16) 3501
25.4%
Uppercase Letter
ValueCountFrequency (%)
T 190
 
12.4%
A 147
 
9.6%
S 112
 
7.3%
L 101
 
6.6%
I 101
 
6.6%
B 85
 
5.5%
D 83
 
5.4%
M 83
 
5.4%
H 78
 
5.1%
W 75
 
4.9%
Other values (16) 477
31.1%
Decimal Number
ValueCountFrequency (%)
1 24
24.0%
0 18
18.0%
2 17
17.0%
5 9
 
9.0%
3 8
 
8.0%
8 8
 
8.0%
9 8
 
8.0%
6 3
 
3.0%
7 3
 
3.0%
4 2
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 782
80.9%
' 76
 
7.9%
! 66
 
6.8%
? 26
 
2.7%
& 7
 
0.7%
: 5
 
0.5%
/ 3
 
0.3%
% 1
 
0.1%
* 1
 
0.1%
Math Symbol
ValueCountFrequency (%)
| 103
98.1%
+ 1
 
1.0%
= 1
 
1.0%
Space Separator
ValueCountFrequency (%)
3015
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15329
78.4%
Common 4220
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1915
 
12.5%
o 1243
 
8.1%
a 1106
 
7.2%
t 1041
 
6.8%
i 1000
 
6.5%
n 970
 
6.3%
r 970
 
6.3%
s 832
 
5.4%
h 676
 
4.4%
l 543
 
3.5%
Other values (42) 5033
32.8%
Common
ValueCountFrequency (%)
3015
71.4%
. 782
 
18.5%
| 103
 
2.4%
' 76
 
1.8%
! 66
 
1.6%
? 26
 
0.6%
1 24
 
0.6%
- 19
 
0.5%
0 18
 
0.4%
2 17
 
0.4%
Other values (16) 74
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3015
15.4%
e 1915
 
9.8%
o 1243
 
6.4%
a 1106
 
5.7%
t 1041
 
5.3%
i 1000
 
5.1%
n 970
 
5.0%
r 970
 
5.0%
s 832
 
4.3%
. 782
 
4.0%
Other values (68) 6675
34.1%

actors
Text

Distinct1617
Distinct (%)99.6%
Missing5
Missing (%)0.3%
Memory size12.9 KiB
2023-11-12T20:54:10.614314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length252
Median length169
Mean length134.03448
Min length10

Characters and Unicode

Total characters217672
Distinct characters62
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1610 ?
Unique (%)99.1%

Sample

1st rowVicky Kaushal|Paresh Rawal|Mohit Raina|Yami Gautam|Kirti Kulhari|Rajit Kapoor|Ivan Rodrigues|Manasi Parekh|Swaroop Sampat|Riva Arora|Yogesh Soman|Fareed Ahmed|Akashdeep Arora|Kallol Banerjee|
2nd rowVicky Ahuja|Shoaib Ibrahim|Shrikant Kamat|Elena Kazan|Vishwas Kini|Major Kishore|Jashn Kohli|Rammy C. Pandey|Manish Sharma|Sparsh Sharma|Farnaz Shetty|Vikas Shrivastav|Chandraprakash Thakur|Brajesh Tiwari|
3rd rowAnupam Kher|Akshaye Khanna|Aahana Kumra|Atul Sharma|Manoj Anand|Arjun Mathur|Suzanne Bernert|Abdul Quadir Amin|Bharat Mistri|Divya Seth|Anil Rastogi|Ramesh Bhatkar|Parrgash Kaur|Jess Kaur|
4th rowEmraan Hashmi|Shreya Dhanwanthary|Snighdadeep Chatterji|Navneet Srivastava|Nanda Yadav|
5th rowMona Ambegaonkar|Ananth Narayan Mahadevan|Devansh Doshi|Arpit Chaudhary|Yamini Singh|Abhay Kulkarni|Veena Nair|Disha Thakur|Kala Ramanathan|Sushant Divgikar|
ValueCountFrequency (%)
singh 119
 
0.7%
ali 75
 
0.4%
sharma 59
 
0.3%
akshay 56
 
0.3%
khan 53
 
0.3%
kay 50
 
0.3%
amitabh 49
 
0.3%
shah 44
 
0.3%
ajay 42
 
0.2%
sanjay 38
 
0.2%
Other values (13793) 17001
96.7%
2023-11-12T20:54:11.198354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 33194
15.2%
15962
 
7.3%
| 15767
 
7.2%
h 14307
 
6.6%
i 13717
 
6.3%
n 12371
 
5.7%
r 11761
 
5.4%
e 9803
 
4.5%
u 6126
 
2.8%
s 6064
 
2.8%
Other values (52) 78600
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 153841
70.7%
Uppercase Letter 31846
 
14.6%
Space Separator 15962
 
7.3%
Math Symbol 15767
 
7.2%
Other Punctuation 229
 
0.1%
Dash Punctuation 27
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 33194
21.6%
h 14307
9.3%
i 13717
 
8.9%
n 12371
 
8.0%
r 11761
 
7.6%
e 9803
 
6.4%
u 6126
 
4.0%
s 6064
 
3.9%
t 5878
 
3.8%
o 5609
 
3.6%
Other values (21) 35011
22.8%
Uppercase Letter
ValueCountFrequency (%)
S 5520
17.3%
K 3589
11.3%
A 3342
10.5%
R 2480
 
7.8%
M 2320
 
7.3%
P 1887
 
5.9%
D 1845
 
5.8%
B 1699
 
5.3%
J 1176
 
3.7%
V 1093
 
3.4%
Other values (16) 6895
21.7%
Other Punctuation
ValueCountFrequency (%)
. 199
86.9%
' 30
 
13.1%
Space Separator
ValueCountFrequency (%)
15962
100.0%
Math Symbol
ValueCountFrequency (%)
| 15767
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 185687
85.3%
Common 31985
 
14.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 33194
17.9%
h 14307
 
7.7%
i 13717
 
7.4%
n 12371
 
6.7%
r 11761
 
6.3%
e 9803
 
5.3%
u 6126
 
3.3%
s 6064
 
3.3%
t 5878
 
3.2%
o 5609
 
3.0%
Other values (47) 66857
36.0%
Common
ValueCountFrequency (%)
15962
49.9%
| 15767
49.3%
. 199
 
0.6%
' 30
 
0.1%
- 27
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 217662
> 99.9%
None 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 33194
15.3%
15962
 
7.3%
| 15767
 
7.2%
h 14307
 
6.6%
i 13717
 
6.3%
n 12371
 
5.7%
r 11761
 
5.4%
e 9803
 
4.5%
u 6126
 
2.8%
s 6064
 
2.8%
Other values (47) 78590
36.1%
None
ValueCountFrequency (%)
á 4
40.0%
ö 3
30.0%
ç 1
 
10.0%
é 1
 
10.0%
ä 1
 
10.0%

wins_nominations
Text

MISSING 

Distinct229
Distinct (%)32.4%
Missing922
Missing (%)56.6%
Memory size12.9 KiB
2023-11-12T20:54:11.379139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length22
Mean length17.520509
Min length5

Characters and Unicode

Total characters12387
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)20.2%

Sample

1st row4 wins
2nd row17 wins & 1 nomination
3rd row3 wins & 5 nominations
4th row6 wins & 3 nominations
5th row1 win
ValueCountFrequency (%)
nominations 447
16.7%
413
15.5%
wins 340
12.7%
1 333
12.5%
nomination 179
6.7%
win 149
 
5.6%
2 146
 
5.5%
3 100
 
3.7%
4 78
 
2.9%
5 61
 
2.3%
Other values (47) 423
15.8%
2023-11-12T20:54:11.733277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 2372
19.1%
1962
15.8%
i 1749
14.1%
o 1262
10.2%
s 789
 
6.4%
a 636
 
5.1%
m 634
 
5.1%
t 631
 
5.1%
1 549
 
4.4%
w 492
 
4.0%
Other values (22) 1311
10.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8598
69.4%
Space Separator 1962
 
15.8%
Decimal Number 1386
 
11.2%
Other Punctuation 413
 
3.3%
Uppercase Letter 28
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2372
27.6%
i 1749
20.3%
o 1262
14.7%
s 789
 
9.2%
a 636
 
7.4%
m 634
 
7.4%
t 631
 
7.3%
w 492
 
5.7%
r 10
 
0.1%
d 8
 
0.1%
Other values (4) 15
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 549
39.6%
2 235
17.0%
3 154
 
11.1%
4 117
 
8.4%
5 90
 
6.5%
6 63
 
4.5%
7 48
 
3.5%
9 46
 
3.3%
0 45
 
3.2%
8 39
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
A 9
32.1%
F 6
21.4%
N 5
17.9%
B 3
 
10.7%
T 3
 
10.7%
O 2
 
7.1%
Space Separator
ValueCountFrequency (%)
1962
100.0%
Other Punctuation
ValueCountFrequency (%)
& 413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8626
69.6%
Common 3761
30.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2372
27.5%
i 1749
20.3%
o 1262
14.6%
s 789
 
9.1%
a 636
 
7.4%
m 634
 
7.3%
t 631
 
7.3%
w 492
 
5.7%
r 10
 
0.1%
A 9
 
0.1%
Other values (10) 42
 
0.5%
Common
ValueCountFrequency (%)
1962
52.2%
1 549
 
14.6%
& 413
 
11.0%
2 235
 
6.2%
3 154
 
4.1%
4 117
 
3.1%
5 90
 
2.4%
6 63
 
1.7%
7 48
 
1.3%
9 46
 
1.2%
Other values (2) 84
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2372
19.1%
1962
15.8%
i 1749
14.1%
o 1262
10.2%
s 789
 
6.4%
a 636
 
5.1%
m 634
 
5.1%
t 631
 
5.1%
1 549
 
4.4%
w 492
 
4.0%
Other values (22) 1311
10.6%

release_date
Text

MISSING 

Distinct1063
Distinct (%)69.8%
Missing107
Missing (%)6.6%
Memory size12.9 KiB
2023-11-12T20:54:12.016280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length21.354139
Min length5

Characters and Unicode

Total characters32501
Distinct characters52
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique724 ?
Unique (%)47.6%

Sample

1st row11 January 2019 (USA)
2nd row11 January 2019 (India)
3rd row11 January 2019 (USA)
4th row18 January 2019 (USA)
5th row11 January 2019 (India)
ValueCountFrequency (%)
india 1127
 
18.6%
usa 355
 
5.9%
september 152
 
2.5%
october 147
 
2.4%
may 135
 
2.2%
august 130
 
2.1%
july 130
 
2.1%
march 126
 
2.1%
january 123
 
2.0%
april 122
 
2.0%
Other values (75) 3507
57.9%
2023-11-12T20:54:12.382751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4532
 
13.9%
0 2400
 
7.4%
2 2348
 
7.2%
a 1782
 
5.5%
1 1694
 
5.2%
) 1520
 
4.7%
( 1520
 
4.7%
n 1382
 
4.3%
e 1370
 
4.2%
i 1272
 
3.9%
Other values (42) 12681
39.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12473
38.4%
Decimal Number 8699
26.8%
Space Separator 4532
 
13.9%
Uppercase Letter 3756
 
11.6%
Close Punctuation 1520
 
4.7%
Open Punctuation 1520
 
4.7%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1782
14.3%
n 1382
11.1%
e 1370
11.0%
i 1272
10.2%
d 1130
9.1%
r 1122
9.0%
u 761
 
6.1%
b 626
 
5.0%
y 505
 
4.0%
t 446
 
3.6%
Other values (11) 2077
16.7%
Uppercase Letter
ValueCountFrequency (%)
I 1130
30.1%
A 608
16.2%
S 511
13.6%
J 372
 
9.9%
U 363
 
9.7%
M 262
 
7.0%
O 147
 
3.9%
N 123
 
3.3%
F 116
 
3.1%
D 93
 
2.5%
Other values (7) 31
 
0.8%
Decimal Number
ValueCountFrequency (%)
0 2400
27.6%
2 2348
27.0%
1 1694
19.5%
3 378
 
4.3%
5 342
 
3.9%
4 331
 
3.8%
6 331
 
3.8%
7 320
 
3.7%
8 287
 
3.3%
9 268
 
3.1%
Space Separator
ValueCountFrequency (%)
4532
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1520
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1520
100.0%
Dash Punctuation
ValueCountFrequency (%)
– 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16272
50.1%
Latin 16229
49.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1782
 
11.0%
n 1382
 
8.5%
e 1370
 
8.4%
i 1272
 
7.8%
d 1130
 
7.0%
I 1130
 
7.0%
r 1122
 
6.9%
u 761
 
4.7%
b 626
 
3.9%
A 608
 
3.7%
Other values (28) 5046
31.1%
Common
ValueCountFrequency (%)
4532
27.9%
0 2400
14.7%
2 2348
14.4%
1 1694
 
10.4%
) 1520
 
9.3%
( 1520
 
9.3%
3 378
 
2.3%
5 342
 
2.1%
4 331
 
2.0%
6 331
 
2.0%
Other values (4) 876
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32500
> 99.9%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4532
 
13.9%
0 2400
 
7.4%
2 2348
 
7.2%
a 1782
 
5.5%
1 1694
 
5.2%
) 1520
 
4.7%
( 1520
 
4.7%
n 1382
 
4.3%
e 1370
 
4.2%
i 1272
 
3.9%
Other values (41) 12680
39.0%
Punctuation
ValueCountFrequency (%)
– 1
100.0%

Interactions

2023-11-12T20:53:59.215185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-12T20:53:58.490319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-12T20:53:58.871419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-12T20:53:59.349514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-12T20:53:58.616456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-12T20:53:58.998535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-12T20:53:59.466228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-12T20:53:58.716047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-12T20:53:59.098485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-11-12T20:54:12.531799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
year_of_releaseimdb_ratingimdb_votes
year_of_release1.0000.1260.125
imdb_rating0.1261.0000.432
imdb_votes0.1250.4321.000

Missing values

2023-11-12T20:53:59.668515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-12T20:53:59.941985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-12T20:54:00.157509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

title_ximdb_idposter_pathwiki_linktitle_yoriginal_titleis_adultyear_of_releaseruntimegenresimdb_ratingimdb_votesstorysummarytaglineactorswins_nominationsrelease_date
0Uri: The Surgical Strikett8291224https://upload.wikimedia.org/wikipedia/en/thumb/3/3b/URI_-_New_poster.jpg/220px-URI_-_New_poster.jpghttps://en.wikipedia.org/wiki/Uri:_The_Surgical_StrikeUri: The Surgical StrikeUri: The Surgical Strike02019138Action|Drama|War8.435112Divided over five chapters the film chronicles the events of the surgical strike conducted by the Indian military against suspected militants in Pakistan occupied Kashmir. It tells the story of the 11 tumultuous events over which the operation was carried out. Indian army special forces carry out a covert operation to avenge the killing of fellow army men at their base by a terrorist group.Indian army special forces execute a covert operation avenging the killing of fellow army men at their base by a terrorist group.NaNVicky Kaushal|Paresh Rawal|Mohit Raina|Yami Gautam|Kirti Kulhari|Rajit Kapoor|Ivan Rodrigues|Manasi Parekh|Swaroop Sampat|Riva Arora|Yogesh Soman|Fareed Ahmed|Akashdeep Arora|Kallol Banerjee|4 wins11 January 2019 (USA)
1Battalion 609tt9472208NaNhttps://en.wikipedia.org/wiki/Battalion_609Battalion 609Battalion 60902019131War4.173The story revolves around a cricket match between the Indian Army and the Pakistan Army gone wrong and narrates the tale of the soldiers of Battalion 609 putting up a fight with Taliban. After an attack on Indian Army that is Battalion 609 near the LOC the match which was to be played between India and Pakistan is cancelled. Anwar Hussein a Pakistani soldier who is very fond of cricket and was looking forward to the match throws a cricket bat at the other end of the LOC and calls the Indian army and Indians a bunch of cowards. In anger the Indian army throws a ball towards them. Soon a verbal fight breaks between them which leads to the two teams deciding to play a cricket match. The losing team will take their post eighteen kilometres back.The story of Battalion 609 revolves around a cricket match between the Indian Army and the Pakistan army gone wrong and narrates the tale of the brave soldiers of Battalion 609 putting up a fight with the mighty Taliban.NaNVicky Ahuja|Shoaib Ibrahim|Shrikant Kamat|Elena Kazan|Vishwas Kini|Major Kishore|Jashn Kohli|Rammy C. Pandey|Manish Sharma|Sparsh Sharma|Farnaz Shetty|Vikas Shrivastav|Chandraprakash Thakur|Brajesh Tiwari|NaN11 January 2019 (India)
2The Accidental Prime Minister (film)tt6986710https://upload.wikimedia.org/wikipedia/en/thumb/a/a1/The_Accidental_Prime_Minister_film.jpg/220px-The_Accidental_Prime_Minister_film.jpghttps://en.wikipedia.org/wiki/The_Accidental_Prime_Minister_(film)The Accidental Prime MinisterThe Accidental Prime Minister02019112Biography|Drama6.15549Based on the memoir by Indian policy analyst Sanjaya Baru The Accidental Prime Minister explores Manmohan Singh's tenure as the Prime Minister of India and the interference of Congress Party in contradicting his decisions during his first tenure. He details how Singh fell a victim to Congress Party's dynastic approach. It highlights how Manmohan Singh was constantly ignored by Congress to maintain turf for Rahul Gandhi the son of Sonia Gandhi and Rajiv Gandhi.Explores Manmohan Singh's tenure as the Prime Minister of India and the kind of control he had over the cabinet and the country.NaNAnupam Kher|Akshaye Khanna|Aahana Kumra|Atul Sharma|Manoj Anand|Arjun Mathur|Suzanne Bernert|Abdul Quadir Amin|Bharat Mistri|Divya Seth|Anil Rastogi|Ramesh Bhatkar|Parrgash Kaur|Jess Kaur|NaN11 January 2019 (USA)
3Why Cheat Indiatt8108208https://upload.wikimedia.org/wikipedia/en/thumb/a/a2/Why_Cheat_India_poster.jpg/220px-Why_Cheat_India_poster.jpghttps://en.wikipedia.org/wiki/Why_Cheat_IndiaWhy Cheat IndiaWhy Cheat India02019121Crime|Drama6.01891The movie focuses on existing malpractices in country's education system the whole concept of buying your way through education jobs and earnings. Even with an evolving education system the country faces scams like SSC and HSC paper leaks CBSE re-examination Vyaapam etc. The movie tries to shift the attention of people to understand the vulnerability of hardworking and gifted students who get left out.The movie focuses on existing malpractices in country's education system the whole concept of buying your way through education jobs and earnings. Even with an evolving education system ...NaNEmraan Hashmi|Shreya Dhanwanthary|Snighdadeep Chatterji|Navneet Srivastava|Nanda Yadav|NaN18 January 2019 (USA)
4Evening Shadowstt6028796NaNhttps://en.wikipedia.org/wiki/Evening_ShadowsEvening ShadowsEvening Shadows02018102Drama7.3280While gay rights and marriage equality has been embraced by most countries a small town in Southern India lives within a cocoon of traditions and social morality. In such a milieu when a young gay man Kartik comes out to his mother Vasudha her entire world comes crashing down. She has no one to turn to dispel her fears and doubts to understand her loving son's truth. Moreover as a woman trapped within a patriarchal conservative society her biggest challenge is to deal with her dogmatic husband Damodar and the conservative society around her. 'Evening Shadows' is a universal story about a mother-son bonding and its emotional strength to withstand the ravages of time and harsh realities.Under the 'Evening Shadows' truth often plays hide and seek. Set in South India and Mumbai 'Evening Shadows' is a tender heartwarming story about a mother-son bond that has to withstand the ravages of time distance and truths.NaNMona Ambegaonkar|Ananth Narayan Mahadevan|Devansh Doshi|Arpit Chaudhary|Yamini Singh|Abhay Kulkarni|Veena Nair|Disha Thakur|Kala Ramanathan|Sushant Divgikar|17 wins & 1 nomination11 January 2019 (India)
5Soni (film)tt6078866https://upload.wikimedia.org/wikipedia/en/thumb/d/de/Soni_India_Netflix_Movie_Poster.jpg/220px-Soni_India_Netflix_Movie_Poster.jpghttps://en.wikipedia.org/wiki/Soni_(film)SoniSoni0201897Drama7.21595Soni a young policewoman in Delhi and her superintendent Kalpana have collectively taken on a growing crisis of violent crimes against women. However their alliance suffers a major setback when Soni is transferred out for alleged misconduct on duty.While fighting crimes against women in Delhi a short-fused policewoman and her level-headed female boss grapple with gender issues in their own lives.NaNGeetika Vidya Ohlyan|Saloni Batra|Vikas Shukla|Mohit Chauhan|Himanshu Kohli|Gauri Chakraborty|Mohinder Gujral|Upasya Goswami|Simrat Kaur|Dimple Kaur|Prateek Pachori|Kalpana Jha|Samar|3 wins & 5 nominations18 January 2019 (USA)
6Fraud Saiyaantt5013008https://upload.wikimedia.org/wikipedia/en/thumb/4/48/Fraud_Saiyyan_film_poster.jpg/220px-Fraud_Saiyyan_film_poster.jpghttps://en.wikipedia.org/wiki/Fraud_SaiyaanFraud SaiyaanFraud Saiyyan02019109Comedy|Drama4.2504Fraud Saiyyan is the story of a con artist in North India who convinces women to marry him just so he can live off their money.Fraud Saiyyan is the story of a con artist in North India who convinces women to marry him just so he can live off their money.NaNArshad Warsi|Saurabh Shukla|Flora Saini|Sara Loren|Varun Badola|Deepali Pansare|Nivedita Tiwari|Peeyush Suhaney|Preeti Sood|Parag Tyagi|Anangsha Biswas|Amanda Rosario|Elli Avrram|NaN18 January 2019 (India)
7Bombairiyatt4971258https://upload.wikimedia.org/wikipedia/en/thumb/b/bb/Bombairiya_poster.jpg/220px-Bombairiya_poster.jpghttps://en.wikipedia.org/wiki/BombairiyaBombairiyaBombairiya02019104Comedy|Crime|Drama4.3295It follows the story of Meghna who gets embroiled in a series of events after her phone gets stolen.It follows the story of Meghna who gets embroiled in a series of events after her phone gets stolen.They didn't mean to change the world.Radhika Apte|Akshay Oberoi|Siddhanth Kapoor|Ravi Kishan|Adil Hussain|Shilpa Shukla|Ajinkya Deo|Amit Sial|NaN18 January 2019 (India)
8Manikarnika: The Queen of Jhansitt6903440https://upload.wikimedia.org/wikipedia/en/thumb/4/44/Manikarnika_Poster.jpg/220px-Manikarnika_Poster.jpghttps://en.wikipedia.org/wiki/Manikarnika:_The_Queen_of_JhansiManikarnika: The Queen of JhansiManikarnika: The Queen of Jhansi02019148Action|Biography|Drama6.57361Manikarnika born in Varanasi when Dixt a minister in Jhansi princely state sees her he proposes her marriage to Gangadar Rao prince of Jhansi.Sadashiv Rao plots along with British officers to conquer Jhansi as he Is promised a stake in it after conquering Jhansi.After the birth of Lakshmi and Gangadar Rao's son Sadashiv Rao sees his place in fear and poison the holy water during the naming ceremony of the child following which he passes away and also leaves Gangadar Rao on death bed.Gangadar Rao decides to adopt a son and Sadashiv Rao feels guilty of not adopting his son instead a toddler who comes running to Lakshmi Bai.After death of Gangadar Rao.The East India Company decides to conquer on Jhansi as there is no male ruler.But Lakshmi Bai decides to take the throne on herself and fight for Jhansi.Story of Rani Lakshmibai one of the leading figures of the Indian Rebellion of 1857 and her resistance to the British Rule.NaNKangana Ranaut|Rimi Sen|Atul Kulkarni|Nalneesh Neel|Danny Denzongpa|Mishti|Kulbhushan Kharbanda|Mohammed Zeeshan Ayyub|Anil George|Vikram Kochhar|Edward Sonnenblick|Ankita Lokhande|Richard Keep|Jishu Sengupta|NaN25 January 2019 (USA)
9Thackeray (film)tt7777196https://upload.wikimedia.org/wikipedia/en/thumb/8/8e/Thackeray_film_poster.jpeg/220px-Thackeray_film_poster.jpeghttps://en.wikipedia.org/wiki/Thackeray_(film)ThackerayThackeray02019120Biography|Drama5.12301Balasaheb Thackrey works as a cartoonist for a newspaper. But his editor isn't happy with it as he creates cartoons of politician which affects the sales of his paper.He is asked to try something else but he quits his job and now his only aim is to fight for the rights of Marathi people. He sees that Marathi people don't have any respect and are made to do all odd works. With a few people he starts to help the needy and gains respect. To fight for the rights he also goes against the law and comes in the eye of politics. After entering politics he starts his own party named after Shivaji Maharaj as Shiv Sena. After giving Marathi Manoos his equal rights his only wish is that one day the Chief Minister of Maharashtra should be a Marathi Manoos.Biographical account of Shiv Sena Supremo Balasaheb Thackeray.NaNNawazuddin Siddiqui|Amrita Rao|Abdul Quadir Amin|Sanjay Narvekar|Prakash Belawadi|Vineet Sharma|Radha Sagar|Sonamoni Jayant Gadekar|Satish Alekar|Micky Makhija|Nikhil Mahajan|Resh Lamba|Jaywant Wadkar|Laxman Singh Rajput|NaN25 January 2019 (India)
title_ximdb_idposter_pathwiki_linktitle_yoriginal_titleis_adultyear_of_releaseruntimegenresimdb_ratingimdb_votesstorysummarytaglineactorswins_nominationsrelease_date
1619Pyaar Zindagi Haitt0847758https://upload.wikimedia.org/wikipedia/en/5/5e/Pyaar_Zindagi_Hai_%282001%29.jpghttps://en.wikipedia.org/wiki/Pyaar_Zindagi_HaiPyaar Zindagi HaiPyaar Zindagi Hai02001157Drama|Romance|Thriller6.639After the marriage of his daughter Geeta with army Major Pratap Singh widower Hridaynath lives a simple middle-class existence with his unmarried daughter Priya in Dehra Dun. Geeta Pratap and their new-born son Munna pay them a visit and live with them for a few days. They find out that Hridaynath is under constant threat from a loan shark he had borrowed money from to get Geeta married. Shortly after that Geeta and Pratap leave back home for Ranikhet and it then that Hridaynath finds out that his loan has been repaid and that they also have a telephone now - thanks to Pratap. Quite unknown to Hridaynath Pratap has set his eyes on Priya and wants her for himself so much so that he is willing to kill Geeta and get re-married to Priya on the pretext that Munna needs looking after. So to get the ball rolling he kills Geeta informs her dad and sister that she has died of a heart attack and convinces them to leave Dehra Dun and live with him in Ranikhet. This is where he ...After the marriage of his daughter Geeta with army Major Pratap Singh widower Hridaynath lives a simple middle-class existence with his unmarried daughter Priya in Dehra Dun. Geeta ...NaNRajesh Khanna|Vikas Kalantri|Ashima Bhalla|Asawari Joshi|Upasna Singh|Mohnish Bahl|Shahbaaz Khan|NaN7 September 2001 (India)
1620Yeh Raaste Hain Pyaar Kett0292740https://upload.wikimedia.org/wikipedia/en/thumb/3/3f/Yehraaste.jpg/220px-Yehraaste.jpghttps://en.wikipedia.org/wiki/Yeh_Raaste_Hain_Pyaar_KeYeh Raaste Hain Pyaar KeYeh Raaste Hain Pyaar Ke02001149Drama|Romance4.0607Two con artistes and car thieves Vicky (Ajay Devgan) and Sakshi (Preity Zintar) run afoul of Bhanwarlal (Deep Dhillon) and his mob when they accidentally kill his brother. Bhanwarlal and his other brother (Mayur) both swear to avenge the death of their brother and mistakenly kill Rohit Verma (also Ajay Devgan) who is a look-alike of Vicky. Sakshi thinks that her love is dead and is devastated. Unknown to Sakshi and Bhanwarlal Vicky is still alive and has taken the place of Rohit fooling Rohit's dad Pratap Verma (Vikram Gokhale); Rohit's wife Neha (Madhuri Dixit) little knowing that Pratap Verma already knows that Rohit is dead but for some strange and mysterious reason permits Vicky to continue with the impersonation.Two con artistes and car thieves Vicky (Ajay Devgan) and Sakshi (Preity Zintar) run afoul of Bhanwarlal (Deep Dhillon) and his mob when they accidentally kill his brother. Bhanwarlal and ...Love is a journey... not a destinationAjay Devgn|Madhuri Dixit|Preity Zinta|Vikram Gokhale|Deep Dhillon|Smita Jaykar|Sunny Deol|Kiran Kumar|Rajeev Verma|Jayshree T.|Asha Sharma|Lalit Tiwari|Shammi|Mayur Verma|NaN10 August 2001 (India)
1621Tum Bintt0290326https://upload.wikimedia.org/wikipedia/en/thumb/1/16/TumBinFilmPoster.jpg/220px-TumBinFilmPoster.jpghttps://en.wikipedia.org/wiki/Tum_BinTum Bin...: Love Will Find a WayTum Bin...: Love Will Find a Way02001158Drama|Romance7.52879Shekar and Amar meet at a party where Amar asks Shekar that he should come and work with him in Canada same night Amar accidentally gets killed after he is hit by Shekar 's car Inspector Dmello swears that he won't close the case until he catches the killer Shekar goes to Amar 's house in Canada and confesses his father about Amar 's death but finds that his father has lost all his body senses after Amar 's death he meets Pia Amar's fiance whose also in depression and trying to save Amar's company Shah Industries Shekar and Pia start working with each other and Shekar falls in love with Pia they both bring Shah Industries back on track and meet Abhi for future business same time Abhi falls in love with Pia Pia also starts liking Shekar but decides to choose Abhi Inspector Dmello comes from India to arrest Shekar Shekar tells Pia that Amar died after he hit his car while confessing all this on phone to Pia a speeding car knocks Shekar following which he is admitted in hospital Amar's...Shekhar accidentally kills his associate Amar and decides to watch over Amar's company and take care of his family and friends. However he falls in love with Amar's fiancé Piya.Love will find a wayPriyanshu Chatterjee|Himanshu Malik|Sandali Sinha|Raqesh Bapat|Vikram Gokhale|Rajesh Khera|Navneet Nishan|6 nominations13 July 2001 (India)
1622Yeh Teraa Ghar Yeh Meraa Ghartt0298606https://upload.wikimedia.org/wikipedia/en/thumb/9/9f/Yeh_Teraa_Ghar_Yeh_Meraa_Ghar_2001_film_poster.jpg/220px-Yeh_Teraa_Ghar_Yeh_Meraa_Ghar_2001_film_poster.jpghttps://en.wikipedia.org/wiki/Yeh_Teraa_Ghar_Yeh_Meraa_GharYeh Teraa Ghar Yeh Meraa GharYeh Teraa Ghar Yeh Meraa Ghar02001175Comedy|Drama5.7704In debt; Dayashankar Pandey is forced to go to Bombay to ask his tenants to vacate his house as it is his only means of acquiring money to pay off his loans. But the problem is these people refuse to move forcing Dayashankar to take matters into his own hands thus making it more difficult for the people to move as situations are in there favors.In debt; Dayashankar Pandey is forced to go to Bombay to ask his tenants to vacate his house as it is his only means of acquiring money to pay off his loans. But the problem is these people...NaNSunil Shetty|Mahima Chaudhry|Paresh Rawal|Saurabh Shukla|Anjan Srivastav|Suhasini Mulay|Asrani|Master Aditi|Radhika Menon|Usha Nadkarni|Nagma|Sanjay Narvekar|Neeraj Vora|1 nomination12 October 2001 (India)
1623Zubeidaatt0255713https://upload.wikimedia.org/wikipedia/en/thumb/f/fc/Zubeidaa_poster.jpg/220px-Zubeidaa_poster.jpghttps://en.wikipedia.org/wiki/ZubeidaaZubeidaaZubeidaa02001153Biography|Drama|History6.21384The film begins with Riyaz (Rajat Kapoor) Zubeida's son setting out to research her life and to meet the people who knew her. The story is thus told in the form of memories/reminiscences. Zubeida is a Muslim actress who's career aspirations are thwarted by her film-producer father (Puri) who looks down upon a woman in acting. He arranges her marriage to her friend's son but that breaks up after the birth of her son. Zubeida is now back at her parent's home sad and depressed. Rose (Lilette Dubey) her father's mistress tries to cheer-up Zubeida by taking her out. On one of these outings Zubeida meets Prince Vijayendra (Victor) Singh of Fatehpur (Bajpai). Quite taken with her the Prince woos and marries her. Zubeida now the 2nd wife of a Hindu prince leaves her son behind with her mother (Sikri) and comes to live at Fatehpur. Here she meets the much older Mandira Devi (Rekha) the Prince's first wife learns about the etiquette of being a Rani and of the duties her husband has ...Zubeidaa an aspiring Muslim actress marries a Hindu prince to become his second wife. Her tumultuous relationship with her husband and her inner demons lead her to a decision which has fatal consequences for them all.The Story of a PrincessKarisma Kapoor|Rekha|Manoj Bajpayee|Rajit Kapoor|Surekha Sikri|Amrish Puri|Farida Jalal|Shakti Kapoor|Lillete Dubey|Ravi Jhankal|Smriti Mishra|S.M. Zaheer|Harish Patel|Seema Bhargava|3 wins & 13 nominations19 January 2001 (India)
1624Tera Mera Saath Rahentt0301250https://upload.wikimedia.org/wikipedia/en/2/2b/Tera_Mera_Saath_Rahen.jpghttps://en.wikipedia.org/wiki/Tera_Mera_Saath_RahenTera Mera Saath RahenTera Mera Saath Rahen02001148Drama4.9278Raj Dixit lives with his younger brother Rahul who is disabled both mentally and physically from birth. Raj is also employed full-time and after work has the responsibility of looking after Rahul which leaves him no time for himself. His beautiful neighbor Suman Gupta is attracted to him but he regards her as a friend only. When Mr. Khanna introduces him to his niece Madhuri both instantly falls in love. The only problem is that Madhuri will only marry Raj if Rahul is institutionalized. Raj agrees to get him admitted in a facility much to Madhuri's delight and together they start to plan their marriage not realizing that things are going to take a turn for the worse.A man is torn between his handicapped brother and his ladylove where he finds difficult whom to choose.NaNAjay Devgn|Sonali Bendre|Namrata Shirodkar|Prem Chopra|Hemu Adhikari|Jayant Sawarkar|Shubhangi Damle|Nilesh Diwekar|Atul Kale|Abhijeet Satam|Daman Baggan|Mangesh Satpute|Shilpa Chauhan|Nirmal Soni|NaN7 November 2001 (India)
1625Yeh Zindagi Ka Safartt0298607https://upload.wikimedia.org/wikipedia/en/thumb/2/21/Yeh_Zindagi_Ka_Safar.jpg/220px-Yeh_Zindagi_Ka_Safar.jpghttps://en.wikipedia.org/wiki/Yeh_Zindagi_Ka_SafarYeh Zindagi Ka SafarYeh Zindagi Ka Safar02001146Drama3.0133Hindi pop-star Sarina Devan lives a wealthy lifestyle with her widowed businessman father Vivek. She soon achieves considerable success and becomes immensely popular. A struggling newspaper Adarsh Times publishes a story about her being born in an orphanage which is received with shock and surprise by Vivek and he instructs his lawyers to file a defamation suit against the owner known as Dada and a Journalist Jay Bhardwaj. Dada and Jay are apologetic however Sarina finds out that she is not Vivek's biological daughter and sets forth to find her parentage. She enlists Jay's assistant and they travel to Ooty where they meet with a Catholic Priest Joseph who refuses to divulge any information to them. Then she meets with a Nun Sister Namrata and subsequently finds out that she is indeed her biological mother. Expecting to be welcomed into her arms she is shocked when Namrata outright rejects her and tells her to return to back to her life with Vivek. But Sarina ...A singer finds out she was adopted when the editor of a struggling tabloid publishes the story.NaNAmeesha Patel|Jimmy Sheirgill|Nafisa Ali|Gulshan Grover|Ehsan Khan|NaN16 November 2001 (India)
1626Sabse Bada Sukhtt0069204NaNhttps://en.wikipedia.org/wiki/Sabse_Bada_SukhSabse Bada SukhSabse Bada Sukh02018\NComedy|Drama6.113Village born Lalloo re-locates to Bombay and returns a wealthy man. He goes to meet his friend Shankar alias Bhompu and together they meet and share tales mostly about women sex and playboy magazines' pictures. A Bollywood movie director is shooting a film nearby and they go and meet him and his beautiful actress Urvashi. After meeting Urvashi Shankar feigns a racking cough and tells his family that he must go to Bombay and seek medical treatment. Together he and Lalloo take the next train to Bombay to see if they can find the biggest happiness in life.Village born Lalloo re-locates to Bombay and returns a wealthy man. He goes to meet his friend Shankar alias Bhompu and together they meet and share tales mostly about women sex and ...NaNVijay Arora|Asrani|Rajni Bala|Kumud Damle|Utpal Dutt|Meeta Faiyyaz|Rabi Ghosh|Tarun Ghosh|Sanjeev Kumar|Keshto Mukherjee|Meena Rai|NaNNaN
1627Daakatt10833860https://upload.wikimedia.org/wikipedia/en/thumb/4/45/Daaka.jpg/220px-Daaka.jpghttps://en.wikipedia.org/wiki/DaakaDaakaDaaka02019136Action7.438Shinda tries robbing a bank so he can be wealthy enough to marry the love of his life and ends up getting caught. But it turns out that he got caught intentionally and has a much bigger plan in mind involving one of Punjab`s biggest criminals.Shinda tries robbing a bank so he can be wealthy enough to marry the love of his life. He ends up getting caught but it turns out that he has a much bigger plan in mind involving one of Punjab's biggest criminals.NaNGippy Grewal|Zareen Khan|NaN1 November 2019 (USA)
1628Humsafartt2403201https://upload.wikimedia.org/wikipedia/en/thumb/4/41/Flag_of_India.svg/23px-Flag_of_India.svg.pnghttps://en.wikipedia.org/wiki/HumsafarHumsafarHumsafar0201135Drama|Romance9.02968Sara and Ashar are childhood friends who share the same status Sara has always had strong emotions for Ashar; while Khirad lives with her mother in a two bedroom apartment. Ashar and Khirad are forced into a marriage due to desperate circumstances. Khirad is a proud and self-sufficient person and does not give importance to wealth and power she only hopes for respect. Ashar believes that he would have married a girl equal to his stature but was forced into a loveless marriage. Slowly their relationship gets stronger and the two find common grounds to begin their marriage. Along with internal struggles between these two strong characters; Sara and those around her plot to create enmity between the newly married couple.Ashar and Khirad are forced to get married due to desperate circumstances. Sara is Ashar's childhood friend and believed she would marry Ashar. Khirad is caught within this love-triangle with other internal and external forces at play.NaNFawad Khan|NaNTV Series (2011–2012)